Science, Faculty of
Earth, Ocean and Atmospheric Sciences, Department of
DSpace
Xue, Luyi
The emergence of iNaturalist has been highly beneficial to scientists by sparking interest in biodiversity and enabling a wide range of community and scientific contributors to generate large, recently documented observational datasets. iNaturalist publishes all research grade observations to the Global Biodiversity Information Facility (GBIF) on a weekly basis and has become the third-largest data contributor to GBIF. iNaturalist offers several advantages, including facilitating connections between researchers and citizen scientists by providing a convenience platform and encouraging broader public participation in biodiversity research. However, iNaturalist also presents several limitations that should be carefully considered when sharing or utilizing its dataset for scientific research. Researchers must pay close attention to issues related to data quality, such as the photo quality and the accuracy of geographic coordinates. In this paper, two case studies are discussed to further illustrate the existing drawbacks of using iNaturalist data. Based on these case studies, several recommendations are proposed to help researchers minimize bias in their studies and assist observers in improving data quality. Addressing these concerns is essential for ensuring the objectivity and reliability of research outcomes.
1 Understanding How iNaturalist Data Affect GBIF and Its Limitations: A Guide to Key Considerations Luyi Xue Major in Environmental Science Department of Earth, Ocean and Atmospheric Science Faculty of Science, University of British Columbia Dr. Michael Lipsen, Linda Jennings May, 2025 2 Table of Content 1. Summary 4 2. Abstract 4 3. Introduction 5 4. GBIF and iNaturalist 6 4.1 GBIF 6 4.1.1 GBIF history 6 4.1.2 How GBIF data uploads from different publishing institutions 7 4.1.3 Brief Overview of GBIF 8 4.2 iNaturalist History 9 4.3 How Database like iNaturalist uploads to GBIF 9 5. Advantages of iNaturalist 10 5.1 Engagement with Students 11 5.2 Engagement with Citizen Science 12 5.3 Facilitation of Scientific Research 13 6. Drawbacks of iNaturalist 14 6.1 Location Coordinates in Observation 15 6.2 Citizen Science Observation Bias 16 6.3 Research Grade Identification 19 6.4 Copyright Release 20 6.5 Photo Quality 21 6.6 Rare Species Identification and Coordinate 22 7. Two Case Studies 24 7.1 Case Study 1 - A Study of the Genus Biden 24 7.2 Case Study 2 - The Drawbacks of iNaturalist brought by Finn McGhee 26 3 8. Suggestions of Using iNaturalist 27 8.1 Things will affect the correctness of research 27 8.2 Recommendations when Using iNaturalist 28 9. Conclusion 29 10. Acknowledgements 30 11. Appendix 31 11.1 Tables and Figures 31 11.1.1 Tables 31 11.1.2 Figures 36 11.2 Glossary 41 12. References 45 4 1. Summary 1. An overview of GBIF and iNaturalist and how they are related. 2. The advantages of using iNaturalist for students, citizen science and scientists. 3. The drawbacks of iNaturalist data and how they can affect scientific research. 4. Two case studies highlight some drawbacks of iNaturalist. 5. Precautions of using iNaturalist for scientific research. 6. Suggestions for users when using iNaturalist to make their observations more useful to researchers. 2. Abstract The emergence of iNaturalist has been highly beneficial to scientists by sparking interest in biodiversity and enabling a wide range of community and scientific contributors to generate large, recently documented observational datasets. iNaturalist publishes all research grade observations to the Global Biodiversity Information Facility (GBIF) on a weekly basis and has become the third-largest data contributor to GBIF. iNaturalist offers several advantages, including facilitating connections between researchers and citizen scientists by providing a convenience platform and encouraging broader public participation in biodiversity research. However, iNaturalist also presents several limitations that should be carefully considered when sharing or utilizing its dataset for scientific research. Researchers must pay close attention to issues related to data quality, such as the photo quality and the accuracy of geographic coordinates. In this paper, two case studies are discussed to further illustrate the existing drawbacks of using iNaturalist data. Based on these case studies, several recommendations are proposed to help researchers minimize bias in their studies and assist 5 observers in improving data quality. Addressing these concerns is essential for ensuring the objectivity and reliability of research outcomes. 3. Introduction To monitor and predict biodiversity change, the essential obligation is to provide a sufficient essential biodiversity variable. Essential biodiversity variables represents the minimum amount of data needed for major taxa groups biodiversity change. To meet this minimum requirement, governments and non-governmental organizations like research centres should utilize all possible data sources, including citizen science datasets (Chandler, 2017). One of the best resources is the GBIF. This is because GBIF is now the most widely used resource that provides open integrated data on species occurrence (Ivanova, 2021). GBIF, as the largest global managing and publishing aggregated data research infrastructure, collected data from 2441 global publishing institutions, such as UBC Beaty Biodiversity Museum, Cambridge University Herbarium and WWF Tanzania. Other relatively large data sources in GBIF like eBird and iNaturalist are citizen science datasets. These citizen science datasets supply roughly half of the data in GBIF (GBIF, 2025). iNaturalist is a successful non-profit application (APP) that is used for uploading observed species for citizen scientists and can help identify species using this APP. The goal of iNaturalist is to build a social network for people to share biodiversity information and build a community to help people learn about nature (iNaturalist, 2024). iNaturalist provides an accessible platform for citizen scientists to engage with research projects and collaborate with other iNaturalist observers (iNaturalist.ca, 2023). To share observations and identifications on iNaturalist, iNaturalist users, also called observers, only need to upload their photo and iNaturalist will automatically come up a species identification suggestions based on the photo. Photographs captured with most newer smartphones typically embed 6 metadata, including the date and location (coordinates), by default. iNaturalist also allows observers to add a short description of the species like leaves’ shape, patterns and flowers and the habitat of observed species. This provides additional information for other iNaturalist observers to identify species. This process significantly reduces the difficulty of species identification compared to traditional methods such as dichotomous keys, thereby attracting millions of users, including biologists and naturalists. By 2021, iNaturalist had contributed over 56 million observations to GBIF (Di Cecco, 2021), and this number had increased to more than 233 million observations by April 11, 2025. This paper provides a brief overview the background of both GBIF and iNaturalist, examining their respective advantages and limitations. The central focus will be on the inherent challenges associated with using citizen science data from iNaturalist. Particular attention will be given to the potential issues that require caution when sharing and utilizing such data in biodiversity research. 4. GBIF and iNaturalist 4.1 GBIF 4.1.1 GBIF history The establishment of GBIF originated from a recommendation made in 1999 by the biodiversity informatics subgroup of the OECD (Organisation for Economic Co-operation and Development) megascience forum. Following the endorsement of this recommendation by science ministers from OECD member states, GBIF was officially established in 2001 through a Memorandum of Understanding signed by participating governments. The overarching goal of GBIF is to develop and maintain a global information infrastructure by 7 advancing science and technology, as a global digital ecosystem of diverse information can support facilitates, coordinates, and globalize the sharing of biodiversity data (GBIF, 2025). GBIF is funded through three main sources. The primary source is core funding from voting participants—countries that adhere to the provisions of the MOU and contribute annual financial support. In addition, supplementary funding may be provided by voting participants or associate country participants. GBIF also receives support from other foundations or institutions that understand and endorse the purpose of GBIF (see Appendix – “Glossary” for detailed definitions) (GBIF, 2025; GBIF, 2025). 4.1.2 How GBIF data upload from different publishing institutions Data publishing institutions in GBIF primarily use two main methods to upload their data. The first method involves uploading data through IPT (Integrated Publishing Toolkits) developed by GBIF (see Appendix Figure 1; GBIF, n.d.). Metadata generated by the IPT is submitted to the GBIF metadata repository, which stores key information such as geographic coordinates, data quality indicators, and author contact details. The data is then transferred to the GBIF registry, which serves as a central coordination mechanism. The registry synthesizes information from various sources and provides a unified view of global biodiversity data, allowing publishers to declare their existence, allocate DOI and organzation names - thereby preparing the datasets for access and download via GBIF.org. The second method involves data publication via external applications (apps) that are integrated with GBIF. These apps automatically publish observation data to GBIF at regular intervals. As of April 11, 2025, GBIF hosts 3,092,638,010 occurrence records, 112,878 datasets, and 12,744 peer-reviewed papers using data from GBIF including all data publishers (GBIF, 2025). Among these peer-reviewed paper, GBIF has documented 3,577 articles in 8 ecological journals, 2,800 in climate change journals, and 1,806 in conservation-related journals. 4.1.3 Brief Overview of GBIF Natural history collections and herbaria around the world hold vast amounts of taxonomic information that have historically been difficult to access dynamically. As institutions digitize and upload their collections to GBIF, biodiversity data becomes more easily shareable and accessible on a global scale. GBIF facilitates international scientific collaboration and serves as a bridge across the digital divide (EDWARDS, 2004). The GBIF database encompasses a wide temporal range, including museum specimen records from the 18th and 19th centuries as well as modern data such as DNA barcodes and smartphone photographs (GBIF, 2025). These aggregated datasets can significantly impact fields such as land management, ecosystem monitoring, and conservation planning (Shirey, 2019). Despite its benefits, caution is required when utilizing GBIF occurrence data. One concern arises from inconsistencies in species naming conventions across countries or regions, which can result in multiple datasets referring to the same species. As noted by James Edwards, Chair of the NSF Directorate for Biological Sciences, such inconsistencies may lead to misinformed conservation actions and hinder international communication regarding endangered species (McCabe, 1999). To address this issue, GBIF developed the “Look Up” tool, which allows users to match species names in CSV files with standardized entries in the GBIF database (GBIF, n.d.). In addition, researchers should be mindful of the accuracy and completeness of data originally uploaded through third-party applications. Since much of this data is contributed by non-experts, errors in identification or incomplete metadata can affect the reliability of analyses based on these datasets. 9 4.2 iNaturalist History iNaturalist was created in 2008 by Nate Agrin, Jessica Kline, and Ken-ichi Ueda as a graduate project at the School of Information at the University of California, Berkeley. In 2011, Ken-ichi Ueda began collaborating with Scott Loarie to further develop and expand the platform through a series of joint efforts. The project became an official initiative of the California Academy of Sciences in 2014 and was subsequently launched in partnership with the National Geographic Society in 2017 (iNaturalist, 2024). In 2023, iNaturalist became an independent non-profit organization, operating in collaboration with various partners through the iNaturalist Network, including GBIF and the International Union for Conservation of Nature (IUCN) (iNaturalist, 2024; iNaturalist, 2022). The extensive volume of data contributed by citizen scientists on iNaturalist can be directly applied to conservation assessments conducted by the IUCN (Gallagher et al., 2024). iNaturalist also automatically imports global conservation status information from the IUCN. Depending on the conservation status, the geographic coordinates of vulnerable observed species may be automatically obscured by iNaturalist to protect them from poacher (iNatForum, 2021; Contreras-Díaz et al., 2023). As of April 12, 2025, iNaturalist hosts 234,021,110 observations, 514,420 documented species, and 8,574,013 registered users. Observations that reach a consensus—defined as two-thirds of the community agreeing on a species identification—are designated as “research-grade” and are subsequently shared with GBIF (iNatHelp, 2024). Observations involving vulnerable species are also made available to the IUCN. Currently, approximately 46.84% of iNaturalist data has been uploaded to GBIF. 4.3 How Database like iNaturalist uploads to GBIF 10 At the 21st meeting of the GBIF Governing Board (2014), the Board formally recognized the importance of data licensing for both data publishers and users accessing content through GBIF (see Appendix, Figure 1 for an overview of data flow between GBIF and its data publishers). As a result, all data publishers — including iNaturalist — are now required to assign one of three Creative Commons licenses to their data: Creative Commons Zero (CC0), which places the data in the public domain; Creative Commons Attribution (CC BY); or Creative Commons Attribution-NonCommercial (CC BY-NC) (see Appendix, glossary for further explanation) (GBIF, 2014). iNaturalist automatically exports all research-grade observations — provided the observer has given permission under one of the three Creative Commons licenses — to GBIF on a weekly basis (see Figure 4). However, these datasets are not necessarily made immediately available on GBIF, as GBIF operates on a separate data ingestion cycle. Depending on the publisher, GBIF may update its datasets monthly or seasonally (Jennings, L., personal communication, 2025). Since species identifications on iNaturalist can change over time with more community input, any updated identifications will be reflected in GBIF following the next scheduled dataset export from iNaturalist (iNaturalist, 2025). Once imported, GBIF integrates the data using Darwin Core standards, which enable the consistent formatting of biodiversity information across sources. This standardization underpins the indexing system on GBIF.org, thereby simplifying processes such as dataset searching, evaluation, and comparison for researchers. As of April 20, 2025, GBIF hosts 3,097,780,109 occurrence records, with 3.55%—or approximately 110,111,085 research-grade observations—contributed by iNaturalist, making it the third-largest data publisher on the platform (GBIF, 2025). 5. Advantages of iNaturalist 11 The emergence of iNaturalist has provided substantial support across various domains. It facilitates student engagement, transforms the landscape of citizen science, and enhances the quality and accessibility of scientific research. 5.1 Engagement with Students iNaturalist, a smartphone application that is also accessible via a web platform, has increasingly been introduced into university fieldwork for species identification. Currently, many instructors have begun incorporating iNaturalist into their teaching—particularly in first-year biology courses—to foster student interest in introductory science and outdoor learning (Unger, 2020). For example, at the University of British Columbia (UBC), BIOL 121 (Genetics, Evolution, and Ecology) and BIOL 324 (Introduction to Seed Plant Taxonomy) utilize iNaturalist for identifying wild plant species and practicing the process of uploading biodiversity observations. One key reason for integrating iNaturalist into coursework is that smartphone-based technology enhances students’ taxonomic identification skills. Compared to traditional methods such as dichotomous keys, iNaturalist significantly reduces both the preparatory workload and the number of fieldwork materials required (e.g., detailed identification guides), streamlining the overall learning process (Unger, 2020). Unger et al. (2020) investigated the effectiveness of iNaturalist in a field-based experiment conducted with students enrolled in a first-year introductory biology course at Wingate University. In the study, students were instructed to initially use dichotomous keys for preliminary identification of observed organisms (e.g., spiders, earthworms, fish), followed by photographing the organisms and identifying them through iNaturalist. After the fieldwork, students completed a survey evaluating their experience and their likelihood of using iNaturalist in future laboratory sessions, measured via a Likert scale. 12 The results showed a higher average rating (above 4.00) for terrestrial observations and a slightly lower rating (between 3.50 and 4.00) for aquatic observations (see Table 5). The accuracy of iNaturalist in identifying terrestrial species was approximately 87%, while the average accuracy for aquatic species (from lakes and streams) was somewhat lower. This reduction was largely attributed to the difficulty of photographing small aquatic organisms clearly, as many are not easily visible to the naked eye. Nevertheless, the correct identification rate for stream organisms remained at a respectable 72.4%, supporting iNaturalist’s credibility as a reliable tool. Importantly, researchers observed that using dichotomous keys before iNaturalist helped reinforce students’ identification abilities. Students demonstrated improved skills and greater confidence when identifying species with iNaturalist following the manual identification step (Unger et al., 2020). 5.2 Engagement with Citizen Science In the past, scientific data collection was largely limited to professional scientists. However, the development of location-aware mobile technologies, sensors, and cameras has significantly advanced the role of citizen science in research (Hochmair et al., 2020). One of the key successes of iNaturalist lies in its ability to enable anyone — regardless of scientific background — to contribute species observations. This open-access model has led to the generation of vast quantities of biodiversity data by a diverse range of participants (Callaghan et al., 2022). The benefits of iNaturalist are not one-sided; it also empowers citizen scientists to actively participate in scientific research. As a standalone app, iNaturalist has built a vibrant and engaged community comprising not only amateur naturalists and enthusiasts, but also experts across various scientific disciplines. This community fosters meaningful connections 13 between the public and professional ecologists or environmental scientists, enabling collaborative efforts in data collection and species identification (McGhee, F., personal communication, 2025). Moreover, iNaturalist helps bridge data gaps by enabling amateur scientists to collect observations in both urban and rural environments—areas that are often deficiency in professionally collected datasets, which typically focus on natural or protected regions. This complementary data collection enhances the comprehensiveness and representativeness of biodiversity databases (Marcenò et al., 2021). Another unique advantage of iNaturalist is its global scope. Users can view observations of rare or geographically distant species from around the world without leaving their homes. Many of these species may not be easily encountered in daily life, but iNaturalist makes it possible to explore global biodiversity directly through a smartphone or computer (McGhee, F., personal communication, 2025). 5.3 Facilitation on Scientific Research With the advancement of electronic device technology, the number of projects involving citizen science has steadily increased (Pocock, 2017). Numerous studies have demonstrated that citizen scientists contribute a significant portion of taxonomic data. Among these contributions, the emergence of iNaturalist has played a pivotal role by supplying large volumes of citizen-generated biodiversity data. This extensive dataset has been instrumental in addressing various ecological and environmental challenges (López-Guillén, 2024). Currently, iNaturalist data are widely applied in disciplines such as biology, ecology, and biogeography. Because iNaturalist observations are collected globally, many researchers have chosen to incorporate its datasets into their studies. For instance, Drury et al. (2019) used iNaturalist records to investigate geographic variation in the wing phenotypes of two 14 damselfly species. Di Cecco et al. (2021) highlighted iNaturalist’s particular usefulness for studying species that are identifiable via photographic evidence. In the Philippines, iNaturalist data contributed to the identification of an endemic and threatened bumblebee, Bombus irisanensis, which was later classified as vulnerable by the IUCN (Wilson et al., 2020). In France, iNaturalist facilitated the tracking of an invasive mantis species, Hierodula patellifera, suspected to have been introduced through human transport (Moulin, 2020). In some cases, researchers examine species interactions by analyzing co-occurrences captured in photographs — such as identifying specific bee species observed landing on flowers (Di Cecco et al., 2021). These observations can also provide indirect clues to species identity based on ecological relationships (McGhee, F., personal communication, 2025). One of the most prevalent applications of iNaturalist data is species distribution modeling, which allows researchers to monitor and predict changes in species ranges using research-grade observations. Such models have been constructed for plants (Chapman et al., 2019), reptiles, amphibians (Fourcade, 2016), and various other organisms. These studies would not have been possible without the voluntary contributions of iNaturalist users (Di Cecco et al., 2021). Another notable strength of iNaturalist is its ability to record observations of endangered, elusive, and rediscovered species. Many of the rediscovered species had not been observed in decades and are critically endangered. Thus, the records contributed by iNaturalist observers play a vital role in conservation efforts (iNaturalist, 2024). 6. Drawbacks of iNaturalist Although iNaturalist is a valuable platform that has made significant contributions to biodiversity research, its drawbacks should not be overlooked. These drawbacks have the potential to introduce biases into scientific analyses. As such, researchers must critically 15 evaluate the quality and representativeness of iNaturalist data before applying it to their studies. This section outlines several potential drawbacks associated with the use of iNaturalist data. 6.1 Location Coordinate in Observation (refers to the observation upload in Figure 2, “start - take photos, record coordinate and notes”) Accurate location coordinates and observation collection dates provide key interpretation of problems like rapid biodiversity loss and climate change (Sterner, 2020). Coordinates also allow researchers to revisit observation sites for species re-identification and to monitor population changes over time (Pawluk, M., personal communication, 2025). Therefore, ensuring coordinate accuracy in iNaturalist is critical for biodiversity research (Figure 2). However, two major issues — inaccurate coordinates and hidden coordinates — continue to hinder the reliability and utility of the data. With the development of smartphone technology, most newer smartphone can automatically embed GPS coordinates in photo metadata. iNaturalist extracts this embedded data, including latitude and longitude, upon photo upload. This process generally yields accurate coordinate information, but slight deviations may occur. Older smartphones, or smartphones with location permissions disabled, may not record coordinates at all. When possible, using dedicated GPS devices or taking a screenshot of a compass application can improve location accuracy (McGhee, F., personal communication, 2025). In some cases, observers use standalone cameras that do not automatically record coordinate data. These users must manually input coordinates, which increases the risk of error. Many citizen scientists are unaware of the importance of precise coordinates and may fail to verify the accuracy of the recorded location when uploading their observations. This 16 lack of awareness can significantly compromise data quality. Using GPS or capturing a screenshot of data from a compass app on a smartphone at the photo location can further improve the accuracy of the recorded coordinates. GBIF, for example, requires the coordinate uncertainty for uploaded data to be within 20 meters to be considered accurate (McGhee, F., personal communication, 2025). If coordinate uncertainty is unknown, it should be reported as “0” (null) (GBIF, 2025). iNaturalist automatically assigns a coordinate uncertainty value when photos contain embedded location metadata (iNaturalist, 2013). Excessive uncertainty may lead to substantial biases in analyses, affecting research outcomes and conservation efforts. Additionally, as the attribution and usage rights of observations in iNaturalist are fully retained by contributors, observers have full control over whether or not to reveal the coordinates of their observations. When the coordinates of an observation are obscured, only the observer retains access to the exact location of rare species (Contreras-Díaz, 2023). This limitation poses a challenge for researchers aiming to study these species in the field. iNaturalist offers two options for researchers to access hidden coordinates. The recommended method is to create a project within iNaturalist and invite observers to join the project as curators, which grants access to private location data. Alternatively, researchers may contact observers individually and request that they change the researcher's account change the “Trusted” status to “Yes” on the relationship page in settings (iNatHelp, 2023). Both approaches require time and effort, creating barriers for researchers attempting to conduct field studies on sensitive or threatened species. 6.2 Citizen Science Observation Bias (refers to the start of Figure 2, “take observations and observations upload”) 17 As a publicly accessible platform, the number of species recorded on iNaturalist is primarily determined by its users, meaning that the dataset is largely shaped by the contributions and behaviors of its community of observers. The ease with which organisms can be observed, users’ location preferences, and the time spent in a single area all significantly affect the quantity and quality of the data collected (Di Cecco, 2021). There is a significant disparity in the observed distribution of different species. Most species recorded by citizen scientists tend to be those commonly found in human-inhabited areas. Additionally, frequently observed species are often either large in size or exhibit distinctive behaviors that make them more noticeable. According to data from 2019, the ten most frequently observed species accounted for 4% of all casual user observations, indicating a strong bias toward more visible or appealing species (Table 3). As a result, despite the vast number of observations on iNaturalist, many taxonomic groups — including some of the most species-rich clades such as Insecta — remain underrepresented, with fewer than 25% of their known extant species recorded on the platform (Di Cecco, 2021). A comparison of Di Cecco’s 2019 list of the most observed species (Table 3) with updated data from April 13, 2025 (Table 1) shows that species popularity has remained relatively consistent. Notably, species such as the mourning dove and the Asian lady beetle have increased in frequency over the past four years. All of the top ten most observed species in both datasets are classified as “Least Concern”. This is likely due to their broad distribution, which may result from the absence of significant dispersal barriers or from their introduction to various regions through human activities. Table 2, which lists the 50 most observed species as of 2025, shows that birds account for 30 of them, reflecting a strong observer preference for avian species (Courter, 2013). This preference may help explain why eBird is currently the largest data contributor to GBIF. However, Table 4, which ranks total observations by organism group, shows that plants and 18 insects together constitute the majority of iNaturalist submissions, totaling 387,236 observations. While birds dominate the top ranks in terms of visibility and frequency, plants and insects are more prevalent overall. Collectively, the tables reveal that iNaturalist users tend to focus on plants, insects, and easily found species such as birds. These trends reflect substantial biases in observation patterns. Observer perceptions and descriptions of species are inherently subjective and may lead to incomplete, exaggerated, or skewed records. This subjectivity can distort species abundance and distribution models. Species status should also be considered a form of citizen science bias, as it can influence the appearance and accuracy of distribution maps. Geographical biases also affect iNaturalist’s dataset. Users from the United States account for 45.5% of the platform's user base and contribute approximately 41.7% of all observations. In contrast, observers from Canada, Mexico, and Australia collectively make up 13.2% of users and 14.7% of observations. Considering number of users in other country, language barriers may not be the primary cause of iNaturalist’s limited adoption in some countries — for instance, Germany and France, both non-English-speaking countries, have 87,875 and 147,643 users respectively. National territory size and population are also unlikely to be the determining factors. For example, China and Russia, despite their vast land areas and large populations, have only 21,562 (0.48%) and 54,406 (1.2%) users respectively. The lack of promotion and visibility of iNaturalist in certain countries, such as China, may be a more critical factor — where the platform is rarely mentioned in classrooms or public discourse. Unfortunately, these biases are difficult to resolve through the platform or its users alone. Consequently, researchers using iNaturalist data — particularly for species distribution modeling or global biodiversity assessments — must critically evaluate the data for geographic and taxonomic imbalances. 19 6.3 Research Grade Identification (Refers to the community agreement section in Figure 2 “community agreement with identification”, and Figure 3) When an iNaturalist observation attains research-grade status, it is automatically uploaded to the Global Biodiversity Information Facility (GBIF) on a weekly basis (Jennings, L., personal communication, 2025; Figure 4). An observation must meet the following six criteria to be designated as research-grade (iNaturalist, 2020): 1. Evidence of observers saw this species: a photograph, sound recording, or video must be provided. 2. What the observer saw: the observer should attempt to identify the genus and species, or select a broader taxonomic group for community refinement. 3. Where the observers found it: the coordinates of the observation site must be recorded. 4. When the observers saw it: the observation must include the actual date and time the organism was observed, not the date the record was uploaded. 5. Include the observers’ user name. 6. Community agreement: at least two-thirds of identifiers must agree on a single taxon, whether that identification is made by the observer or other users. While these requirements appear to ensure scientific rigor, in practice, an observation can attain research-grade status with only two agreeing identifications — including the observer's own — as illustrated in Figure 2. In such cases, even when only one additional user confirms the observer's identification, the observation is labeled “Research Grade” and flagged as uploaded to GBIF (Figure 3). This minimal threshold introduces a high risk of 20 misidentification, particularly when observers can simply request a friend to confirm their identification, thereby bypassing broader community validation. Therefore, under such circumstances, research-grade observations should not be regarded as entirely reliable. Researchers should exercise greater caution when using data from species that are easily confused with morphologically similar ones. The issue is further exacerbated in the case of rare or lesser-known species. These species may lack distinctive features or may be subject to preconceived judgments by non-experts, making them more susceptible to misidentification. Moreover, when only two users — neither of whom may be specialists — confirm the identification, the observation still qualifies as research-grade, despite the higher likelihood of error. This process can introduce inaccuracies into databases such as GBIF. For these reasons, observations of rare or cryptic species should ideally require confirmation from at least one expert before being considered reliable. Greater scrutiny is needed for such records to ensure that research based on iNaturalist data maintains scientific integrity. 6.4 Copyright Release (Refers to the copyright section in Figure 2, “release copyright in iNaturalist”) What many users may not realize is that, under most legal systems, content such as photographs is protected as a form of intellectual property. In order for iNaturalist observations to be eligible for integration into GBIF, users must license their content under one of three Creative Commons licenses: CC0, CC BY, or CC BY-NC (iNaturalist, 2021). Upon first use, iNaturalist prompts users to choose whether their photos and observations may be used by scientists. However, users are free to decide how much of their data is shared and under what conditions. Some individuals initially select “No license (all 21 rights reserved),” thereby preventing any redistribution of their content. Many forget to update this setting later, even after submitting large volumes of data. As a result, their research-grade observations remain excluded from external platforms such as GBIF. To resolve this, observers can navigate to "Content and Display" in their account settings and manually adjust the license type for their content (iNaturalist, 2021). While this setup is relatively simple, a substantial number of users overlook the importance of licensing, inadvertently limiting the scientific utility of their contributions. According to data from April 13, 2025, approximately 38,890,891 research-grade observations—26.2% of the total—were not uploaded to GBIF. Although not all of these cases were due to copyright restrictions, a significant portion failed to meet licensing requirements (iNaturalist, 2025; GBIF, 2025). Despite the simple process of releasing copyright, many observations are excluded from GBIF due to observers' neglect of copyright settings (McGhee, F., personal communication, 2025). 6.5 Photo Quality iNaturalist’s automated identification suggestions are highly dependent on the quality of submitted photographs. Low-quality images—arising from a variety of issues—can significantly compromise the platform's ability to provide accurate taxonomic suggestions (McGhee, F., personal communication, 2025). One of the primary factors influencing photo quality is resolution. Higher-resolution images increase the likelihood of correct identification across both terrestrial and aquatic organisms. However, achieving such clarity can be difficult. Small organisms, for example, are inherently harder to photograph in detail, and aquatic species may require specialized techniques to capture high-resolution images due to their environment. In the case of trees and shrubs, a clear visible key identifying features such as leaf edges and venation patterns is 22 necessary, especially for accurate genus or species-level identification (Unger et al., 2020). Consequently, blurry or poorly composed images are likely to result in incorrect identification suggestions by iNaturalist. In addition to resolution, image frame and representativeness are also critical. López-Guillén et al. (2024) emphasize that the framing and content of a photo must effectively capture relevant morphological features. Incomplete or unrepresentative images limit the ability to classify specimens accurately. For plants in particular, an ideal submission includes photos of various parts—such as leaves, flowers, fruits—as well as the surrounding habitat and a reference for scale (e.g., ruler, finger, or coin) (López-Guillén et al., 2024; Pawluk, M., personal communication, 2025; McGhee, F., personal communication, 2025). High-quality image sets can help distinguish between species with subtle morphological differences. For instance, the only reliable distinguishing feature between Bidens amplissima and Bidens tripartita is the shape of the flower. Therefore, a photograph that does not include the flower makes it impossible to determine the correct species (Pawluk, M., personal communication, 2025). 6.6 Rare Species Identification and Coordinate On iNaturalist, two major challenges associated with rare species are accurate identification and the obscuration of their coordinates. The AI-powered suggestion system in iNaturalist is trained on its existing dataset, which is heavily skewed toward common species. As a result, the model often misidentifies rare species as more common, visually similar ones (McGhee, F., personal communication, 2025). As of April 2017, the model had been trained on approximately 5,000,000 “verifiable” observations (including photos, location data, and other metadata), among which 2,500,000 were classified as research-grade. Within this dataset, around 13,730 species had at least 20 23 research-grade observations. Consequently, about 85% of new observations fall within this labeled set of species, while the remaining 15% belong to the "long tail"—out-of-sample species with fewer data points (iNaturalist, 2024). This distribution enables iNaturalist to perform well on identifying common species but highlights the model’s limitations when dealing with rare taxa. These misidentifications often require correction by taxonomic experts or experienced members of the iNaturalist community. However, given the platform’s massive volume of daily uploads—particularly for common species—many errors remain undetected due to limited expert availability and the time-intensive nature of manual review. Another challenge arises from iNaturalist’s intentional obscuration of precise coordinates for species listed on the IUCN Red List. This policy aims to prevent poaching and minimize habitat disturbance (Contreras-Díaz, 2023). While well-intentioned, this protective measure poses significant obstacles for researchers attempting to assess biodiversity patterns or monitor species distributions over time. For many researchers, field verification is essential to assess changes in species distribution and population size. However, when iNaturalist automatically obscures the coordinates of rare species, direct inspection becomes impossible. Although this privacy measure is well-intentioned, it has created significant challenges for scientists studying rare taxa (see Section 7.1, Case Study 1: A Study of the Genus Bidens). In such cases, researchers must contact individual observers to request access to the original coordinates—a process that is both time-consuming and impractical at scale. To address this, some researchers have turned to data-cleaning techniques to minimize spatial uncertainty without needing to contact observers. For instance, Song et al. (2024) referenced a method proposed by Scheldeman and Zonneveld (2010), where occurrence records are overlaid onto an alpha-hull shapefile to eliminate geographically implausible points. Additionally, R packages such as spThin, biomod2, and dismo were employed to further reduce coordinate noise and improve species distribution modeling. However, this 24 approach is only a partial solution, and its accuracy is limited; it should be considered a last resort when direct access to precise location data is unattainable (Song et al., 2024). Due to the percieved value of rare species are influenced from region difference, iNaturalist allows observers to choose whether to mark an observation as sensitive or rare. But the level of protection applied often depends on how informed the observer is about the species’ conservation status. For example, Malaxis brachypoda is classified as “yellow-listed” (no risk of extinction) in the British Columbia Conservation Status (E-Flora BC, n.d.). While in the United States most coordinates for this species are hidden due to its protected status, in Canada, the majority of Malaxis brachypoda observations remain publicly visible (GBIF, 2025). As a result, how an observer understands the status of a species significantly influences the visibility of its location data. 7. Two Case Studies 7.1 Case Study - A Study of the Genus Biden The first case study involves Megan Pawluk, a PhD candidate at the University of British Columbia. Pawluk is researching Bidens amplissima, a vulnerable species classified as Near Threatened by the IUCN and Blue-listed in the BC Conservation Status (Lansdown, 2017; E-Flora BC, n.d.). To investigate the population and distribution of Bidens amplissima in British Columbia, Pawluk examined iNaturalist records for this species and its two closely related congeners: Bidens cernua and Bidens tripartita. Among them, B. cernua is the most frequently confused with B. amplissima due to their overall morphological similarity, particularly in their floral structures. However, the two can be distinguished by leaf morphology and habitat preferences, as B. amplissima is typically confined to a specific type of habitat (E-Flora BC, n.d.). For B. tripartita, the flowers and fruits differ significantly from those of B. amplissima, 25 but their foliage appears remarkably similar at the whole-plant level. This similarity makes it difficult to accurately distinguish between the two based solely on leaf characteristics—especially during times of the year when flowers and fruits are not present. Through her searches on iNaturalist, Pawluk discovered potentially new populations and distribution ranges of B. amplissima, including several records on Vancouver Island that had not been previously documented in the BC Conservation Data Centre. However, due to morphological similarities among the three species, misidentifications on iNaturalist are common. This is because iNaturalist’s automated identification tool, which is primarily trained on common species, tends to suggest B. cernua or B. tripartita rather than B. amplissima, as mentioned in section 6.6 (McGhee, F., personal communication, 2025). To address this, Pawluk systematically reviewed iNaturalist observations for all three species, checking for potential misclassifications. This task was particularly challenging when user-submitted photographs were of low quality (see Section 6.5). In such cases, it was often impossible to determine the correct species based on a single, unclear image. As several researchers have emphasized, optimal species documentation should include clear photographs of flowers, leaves, fruits (when available), the entire plant, and a visible scale (e.g., a coin or ruler), along with notes on morphology (López-Guillén et al., 2024). Unfortunately, many untraining iNaturalist users might only upload one photo with little to no accompanying information. Once Pawluk confirmed that an observation was likely B. amplissima, she used the associated coordinates to conduct field visits for in-person verification. However, in several cases, the coordinates were obscured by the observer. In those instances, Pawluk had to contact individuals directly to request access to the precise locations. According to her comments about iNaturalist, Pawluk identified two primary barriers to effective species validation: (1) insufficient photo quality, and (2) lack of morphological 26 notes. Some users only submitted a single image of the entire plant, which proved inadequate for both the automated suggestion system and researchers seeking to confirm the species’ identity. 7.2 Case Study 2 - The Drawbacks of iNaturalist brought by Finn McGhee The second case study is from Finn McGhee, a botanist based in Vancouver and a member of the BC Parks iNaturalist Project 2024 team (iNaturalist, n.d.). He joined iNaturalist in 2020 and, as of April 2025, has contributed 108,878 observations and 30,438 identifications. As an experienced iNaturalist user, McGhee is familiar with both the platform’s strengths and its drawbacks. When observers upload photos, iNaturalist provides several identification suggestions based on the image content. However, since the autosuggestion model is primarily trained on common species, the algorithm tends to favor these over rare ones. As a result, iNaturalist often misidentifies rare species as their more common lookalikes. This issue is particularly pronounced when photos are of low quality or lack key identifying features. Many organisms within the same genus or family share similar traits, which increases the likelihood of misclassification. If a rare species is incorrectly labeled as a common one, it can be difficult to rediscover and reclassify it within the larger dataset of common species. Although rare species may have numerous observations, only a small proportion of these typically meet the quality standards necessary for research. After filtering out ambiguous or unreliable data, researchers often find only a few usable records. According to McGhee, this is largely due to observers being unaware of key factors that affect data quality. For instance, the absence of a size reference in photos is a common issue. Including a scale—such as a coin, finger, or ruler—greatly aids in verifying the species by size. Other major concerns include imprecise coordinates and poor image quality, both have been discussed previously. 27 He also emphasized the importance of precise coordinates. When coordinate data is inaccurate, the corresponding observation may be excluded during data filtering — even if it represents a previously undocumented population. McGhee also highlights the value of documenting species interactions. For example, if a parasite is observed near a host organism, photographing the parasite can assist in the host’s identification. Such interactions can provide valuable contextual clues for classification. In summary, McGhee considers that iNaturalist users should be aware of the platform’s inherent limitations, as well as avoidable issues caused by observers themselves. Improving photo quality, including accurate metadata, and paying attention to ecological context can significantly enhance the utility of iNaturalist data for scientific research. 8. Suggestions of Using iNaturalist 8.1 Things to aware that could affect the correctness of research To ensure data accuracy and minimize potential issues during research, researchers should remain cautious of the following factors when using iNaturalist data: ● Coordinate Accuracy: Coordinates with low spatial precision may fail to accurately reflect a species’ true habitat or distribution. Accurate location data is not only crucial for studies involving species distribution, habitat modeling, and conservation planning—especially for rare species—but also improves the performance of iNaturalist’s computer vision model. The system uses geographic data to help weight its identification suggestions, meaning that precise coordinates can significantly enhance the accuracy of species predictions. ● Photo Quality: Low-quality images may lead to misidentifications or the inclusion of incorrect species within the dataset. This can compromise data reliability, distort analysis results, and potentially lead to erroneous scientific conclusions. 28 ● Research Grade Bias: iNaturalist designates observations as "research-grade" when at least two users agree on an identification. However, this does not guarantee taxonomic accuracy—particularly for rare or morphologically similar species. As a result, even research-grade observations may contain misidentifications and should be critically evaluated before use. 8.2 Recommendations when Using iNaturalist Based on suggestions from Megan Pawluk and Finn McGhee, several key areas for improvement have been identified to help iNaturalist observers contribute higher-quality data: ● Include Descriptive Notes: Observers should add notes to their submissions that describe the organism’s physical appearance, surrounding habitat, co-occurring animals, and any visible parasites. Photos may not fully capture all relevant characteristics—such as plant height or fine leaf structures—so supplementary written descriptions are especially valuable. For animals, which can be difficult to photograph in detail, written observations become even more critical. Additionally, explaining how the observer arrived at the identification can help others verify or refine it. ● Capture Multiple Detailed Photos: When possible, observers should photograph multiple features of the species, including the full shape of leaves (both front and back), flowers, fruit (if present), and a reference for plant height and size. The inclusion of more morphological details significantly enhances the likelihood of accurate identification, especially for species with close visual similarities. ● Record Accurate Coordinates: Some smartphone models do not automatically embed GPS data in photos. Given the importance of precise location data for ecological and conservation research, observers are encouraged to manually record 29 coordinates using a GPS device or map application and include them when uploading observations. ● Verify iNaturalist Suggestions with Caution: While iNaturalist’s identification tool performs well with common species, it can be inaccurate when photos are unclear or lack diagnostic features. Observers should not rely solely on AI suggestions; instead, they should cross-check with dichotomous keys or other traditional identification resources to improve accuracy. 9. Conclusion Overall, as one of the most successful citizen science platforms, iNaturalist has significantly contributed to ecological and environmental research by bridging the gap between citizen observations and scientific inquiry. It offers an accessible platform for non-experts to engage in biodiversity studies and has inspired widespread public participation in scientific data collection. This influx of observational data has helped address many research questions and advanced understanding in various fields. However, despite its value, iNaturalist—currently a major data provider to GBIF, with over 110 million research-grade observations—still has several limitations that warrant attention. While the platform attempts to improve data reliability by designating observations as “research-grade,” this label does not always guarantee scientific accuracy, especially in the case of rare or easily misidentified species. As such, users should remain cautious about potential data quality issues when using iNaturalist in academic or applied research. Researchers must also be aware of observer-related biases—such as uneven geographic coverage or differences in identification skill—which are often difficult to avoid but should be acknowledged and addressed in study design or analysis. 30 10. Acknowledgments Special thanks to my supervisors Linda Jennings and Michael Lipsen for their support and guidance to this research. Also sincerely appreciate the support and helpful suggestions provided by Megan Pawluk and Finn McGhee through personal communication. The completion of this paper could not have been possible without their help. 31 11. Appendix 11.1 Tables and Figures 11.1.1 Tables Table 1. The ten most observed species in iNaturalist and their observation counts as of April 13, 2025. Asian lady beetles and rock doves appeared on the list in 2025. Common Name Scientific Name Order Number of Observations Ranking Change Mallard Anas platyrhynchos Anseriformes 667,311 From No.3 to No.1 Western Honey Bee Apis mellifera Hymenoptera 510,381 No change House Sparrow Passer domesticus Passeriformes 396,313 From No.7 to No.3 Asian Lady Beetle Harmonia axyridis Coleoptera 367,037 Not on the list in 2021 Monarch Butterfly Danaus plexippus Lepidoptera 362,498 From No.1 to No.3 Canada Goose Branta canadensis Anseriformes 340,023 From No.5 to No.6 American Robin Turdus migratorius Passeriformes 336,240 From No.9 to No.7 32 Great Blue Heron Ardea herodias Pelecaniformes 331,499 From No.4 to No.8 Rock Dove Columba livia Columbiformes 305,041 Not on the list before Great Egret Ardea alba Pelecaniformes 298,598 From No.7 to No.10 Total 3,914,941 Table 2. Number of classes represented by the top 50 most observed species in iNaturalist as of April 13, 2025. Birds accounted for 60% of the most popular species. Number Birds 30 Insects 7 Monocots and Dicots 6 Mammals 3 Amphibians 3 Aquatic species 1 Table 3. The ten most observed species in iNaturalist through 2019 and their observation counts. As of April 13, 2025, the red-tailed hawk had dropped to No. 14 and the eastern gray 33 squirrel to No. 12. The remaining species remain among the “Ten Most Popular Observations.” Note. Reprinted from “Natural History Collections Can Address Environmental Change,” by J. A. Cook, M. A. Aubry, J. E. Light, et al., 2021, BioScience, 71(11), p. 1181 (https://doi.org/10.1093/biosci/biab093). Copyright 2021 by Oxford University Press. Used with permission. Table 4. Number of iNaturalist observations by organism group, as recorded on April 13, 2025. The groups are listed in descending order from most to fewest observations. 34 Group of Organism Number Insects 211,237 Plants 175,999 Fungi 26,046 Mollusc 19,720 Fish 16,381 Spiders 16,116 Birds 11,292 Amphibians 9,264 Frogs 6,013 Mammals 4,604 Table 5. Responses of 81 undergraduate students based on their outdoor activities. Row 2 shows how much they liked using iNaturalist to identify species in streams, lakes, trees, and litter. Row 3 shows their willingness to use iNaturalist in future laboratory sessions. Row 4 shows the average correct identification rate by iNaturalist based on region. Note. Adapted from “Improving Data Literacy Through Increased Student Engagement With Citizen Science Datasets,” by T. G. Green, 2020, The American Biology Teacher, 82(4), p. 236 (https://doi.org/10.1080/00219266.2020.1739114). Copyright 2020 by the National Association of Biology Teachers. Published by Taylor & Francis. Used with permission. 35 36 11.1.2 Figures Figure 1. How data publishers around the world upload their data to GBIF. Data publishers use IPT (a tool developed by GBIF) to publish their data to the GBIF metadata repository, where GBIF stores all submitted datasets. The data is then sent to the GBIF registry to receive a DOI for research use. Once the data has been processed by the registry, it becomes available for research and analysis. 37 Figure 2. How observations in iNaturalist are uploaded to GBIF. After an observation is uploaded to iNaturalist, it awaits confirmation from other users in the iNaturalist community. When more than two-thirds of the community agree on an identification, the observation becomes research grade and is published to GBIF, provided the observer has granted the appropriate copyright license. iNaturalist typically publishes all eligible data to GBIF once a week. Scientists may return later to recheck or confirm identifications over time. 38 User_1 User_1 User_2 UBC Botanical Garden, Vancouver, BC, CA 39 User_1 40 Figure 3. An example of how an observation of Achillea millefolium appears in iNaturalist once it becomes research grade. In the bottom-left corner, the quality grade is marked as “Research,” and in the bottom-right corner, it states “This observation is featured on 1 site: GBIF!” All observations published to GBIF will appear in a similar format. Figure 4. The process of how iNaturalist data is published to GBIF. After an observation becomes research grade, it is published to GBIF during the platform’s weekly data upload. 41 Figure 5. How observations identification changes in iNaturalist are reflected in GBIF uploads. When the identification of an observation is updated in iNaturalist and confirmed by at least two-thirds of the community, the new identification is published to GBIF during iNaturalist’s next data release. The previous version of the observation is replaced by the updated one. 11.2 Glossary 1. GBIF Membership 1.1 Voting Participants: Voting Participants are countries could observe the MOU provisions and annually funding GBIF budget. These countries have voting right on GBIF Governing Board and other benefit 42 1.2 Associate Country Participants: Associate Country Participants may join the Governing Board discussions but no rights to vote. They also don’t need to make any financial contribution to GBIF. 1.3 Other Associate Participants: Other Associate Participants can be either international organizations, intergovernmental organizations or economic international scope organizations. They can attend the GBIF Governing Board, endorse data pubolishers and collaborate in regional meetings by mentoring and training programs or other activities. They don’t have the access to become a Voting Participants, and funds not need. 2. Creative Commons Zero (Public Domain Dedication) (CC0): A license allows creators to waive all copyright and related rights in their works, effectively dedicating them to the public domain. Users can use the work for any purpose, without requiring attribution 3. Creative Commons Attribution (CC BY): A license allows others to distribute, remix, adapt, and build upon your work, even for commercial purposes, as long as they give appropriate credit to you. 4. Creative Commons Attribution-NonCommercial (CC BY-NC): A license lets others remix, adapt, and build upon your work non-commercially. Although their new works must acknowledge you and be non-commercial, they don’t have to license their derivative works on the same terms. 5. Darwin Core: Darwin Core Standard (DwC), which is usually called Darwin Core, offers a stable and flexible framework for compelling biodiversity data from different sources. 43 6. Likert scale (µ): It is a rating scale that measures the attitude and motivation of participants. Participants can choose between “strongly agree” to “strongly disagree”. Usually use 5-point or 7-point scales with a neutral option included. 7. IPT(Integrated Publishing Toolkit): IPT is an open-source software developed by GBIF that able for organizations around the world to creat, manage, and share biodiversity datasets through a standardized repository. 8. GBIF Metadata Repository: A centralized system for describing and accessing biodiversity datasets within the GBIF network. 9. GBIF Registry: GBIF registry is the core component of the architecture responsible to serve informantion on Participants (Nodes), institutions (data publishers), dataset, network etc. It also provides the tool to identitfy and access data. 10. Pensoft Online Editor System: a web-based collaborative writing and publishing platform developed by Pensoft Publishers. It is primarily used for creating, editing, and submitting scientific manuscripts, especially in the fields of biodiversity, ecology, and environmental sciences. 11. spThin r package: This is a set of functions used to spatially thin the occurence data of species. It will resulte a thinned data can be used in ecological modeling, such as ecological niche modeling. 12. biomod2 r package: This package provides tools for species distribution modeling, model calibration and evaluation, ensemble modeling, forecasting, and visualization. It supports running up to 10 individual models using presence/absence or presence/pseudo-absence data, and allows users to combine them into ensemble models and projections. Additional evaluation and visualization features are also included. 44 13. dismo r package: A methodology for modeling species distribution. It could predict the environmental similarity of any site to that of the locations of known occurrences of a species. 45 12. References Callaghan, C. T., Mesaglio, T., Ascher, J. S., Brooks, T. M., Cabras, A. A., Chandler, M., Cornwell, W. K., Cristóbal Ríos-Málaver, I., Dankowicz, E., Urfi Dhiya'ulhaq, N., Fuller, R. A., Galindo-Leal, C., Grattarola, F., Hewitt, S., Higgins, L., Hitchcock, C., James Hung, K., Iwane, T., Kahumbu, P., . . . Young, A. N. (2022). The benefits of contributing to the citizen science platform iNaturalist as an identifier. PLoS Biology, 20(11), e3001843. https://doi.org/10.1371/journal.pbio.3001843 Chandler, M., See, L., Copas, K., Bonde, A. M. Z., López, B. C., Danielsen, F., Legind, J. K., Masinde, S., Miller-Rushing, A. J., Newman, G., Rosemartin, A., & Turak, E. (2017). Contribution of citizen science towards international biodiversity monitoring. Biological Conservation, 213, 280-294. https://doi.org/10.1016/j.biocon.2016.09.004 Chapman, D., Pescott, O. L., Roy, H. E., & Tanner, R. (2019). Improving species distribution models for invasive non-native species with biologically informed pseudo-absence selection. Journal of Biogeography, 46(5), 1029-1040. https://doi.org/10.1111/jbi.13555 Contreras-Díaz, R. G., Nori, J., Chiappa-Carrara, X., Peterson, A. T., Soberón, J., & Osorio-Olvera, L. (2023). Well-intentioned initiatives hinder understanding biodiversity conservation: Cloaked iNaturalist information for threatened species. Biological Conservation, 282, 110042. https://doi.org/10.1016/j.biocon.2023.110042 Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A., & Kaiser, E. W. (2013). Weekend bias in citizen science data reporting: Implications for phenology 46 studies. International Journal of Biometeorology, 57(5), 715-720.https://doi.org/10.1007/s00484-012-0598-7 Di Cecco, G. J., Barve, V., Belitz, M. W., Stucky, B. J., Guralnick, R. P., & Hurlbert, A. H. (2021). Observing the observers: How participants contribute data to iNaturalist and implications for biodiversity science. Bioscience, 71(11), 1179-1188. https://doi.org/10.1093/biosci/biab093 EDWARDS, J. L. (2004). Research and societal benefits of the global biodiversity information facility. Bioscience, 54(6), 486-487. https://doi.org/10.1641/0006-3568(2004)054[0486:RASBOT]2.0.CO;2 E-Floral BC. (n.d). Bidens amplissima Greene. E-FLORA BC: ELECTRONIC ATLAS OF THE FLORA OF BRITISH COLUMBIA. https://linnet.geog.ubc.ca/Atlas/Atlas.aspx?sciname=Bidens%20amplissima E-Floral BC. (n.d). Bidens cernua L.. E-FLORA BC: ELECTRONIC ATLAS OF THE FLORA OF BRITISH COLUMBIA. https://linnet.geog.ubc.ca/Atlas/Atlas.aspx?sciname=Bidens%20cernua&redblue=Both&lifeform=7 E-Floral BC. (n.d). Bidens tripartita L.. E-FLORA BC: ELECTRONIC ATLAS OF THE FLORA OF BRITISH COLUMBIA. https://linnet.geog.ubc.ca/Atlas/Atlas.aspx?sciname=Bidens%20tripartita Fourcade, Y. (2016). Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change. Ecological Informatics, 36, 8-14. Gallagher, R., Roger, E., Packer, J., Slatyer, C., Rowley, J., Cornwell, W., Ens, E., Legge, S., Simpfendorfer, C., Stephens, R., & Mesaglio, T. (2024). 47 Incorporating citizen science into IUCN red list assessments. Conservation Biology, 39(2), e14329-n/a. https://doi.org/10.1111/cobi.14329 GBIF. (n.d). Data quality requirements: Occurrence datasets. GBIF. https://www.gbif.org/data-quality-requirements-occurrences#:~:text=With%20scientific%20names%2C%20there%20are,help%20interpretation%20and%20error%20prevention. GBIF. (n.d). Funders. GBIF. https://www.gbif.org/funders GBIF. (2010). GBIF Memorandum of Understanding. GBIF. https://doi.org/10.15468/doc.ajz7-qt28 GBIF. (n.d). Malaxis brachypoda (A.Gray) Fernald. GBIF. https://www.gbif.org/occurrence/search?continent=NORTH_AMERICA&country=US&taxon_key=2808231&year=2023 GBIF. (n.d.). TOOLS | LOOK UP. GBIF. https://www.gbif.org/tools/species-lookup GBIF. (n.d). What is Darwin Core, and why does it matter?. GBIF. https://www.gbif.org/darwin-core GBIF. (n.d). What is GBIF. Global Biodiversity Information Facility. https://www.gbif.org/what-is-gbif Hochmair, H. H., Scheffrahn, R. H., Basille, M., & Boone, M. (2020). Evaluating the data quality of iNaturalist termite records. PloS One, 15(5), e0226534-e0226534. https://doi.org/10.1371/journal.pone.0226534 iNatHelp. (2023, December 4). How can I access the hidden coordinates of sensitive species?. iNaturalist. https://help.inaturalist.org/en/support/solutions/articles/151000170343-how-can-i-access-the-hidden-coordinates-of-sensitive-species- 48 iNatHelp. (2024, July 30). What is the Data Quality Assessment and how do observations qulify to become “Research Grade”?. iNatHelp. https://help.inaturalist.org/en/support/solutions/articles/151000169936-what-is-the-data-quality-assessment-and-how-do-observations-qualify-to-become-research-grade-#:~:text=Observations%20become%20Research%20Grade%20when,good%20as%20it%20can%20be iNatForum. (2021). Updating IUCN Red List conservation statuses (blogs). iNatForum. https://forum.inaturalist.org/t/updating-iucn-red-list-conservation-statuses/25712 iNaturalist. (2024, September 10). About iNaturalist. iNaturalist. https://www.inaturalist.org/pages/about iNaturalist. (n.d). Current official iNaturalist help documentation can now be found at iNaturalist Help. iNaturalist community. https://www.inaturalist.org/pages/archived+help#GBIFdata iNaturalist. (n.d). Finn McGhee. iNaturalist.ca. https://inaturalist.ca/people/fmcghee iNaturalist. (2024, October 17). Hundreds of lost species rediscovered on iNaturalist. iNaturalist. https://www.inaturalist.org/blog/100442-inaturalist-and-re-wild-partnering-to-rediscover-hundreds-of-lost-species iNaturalist. (2024, January 9). iNaturalist Computer Vision Explorations. iNaturalist. https://www.inaturalist.org/pages/computer_vision_demo iNaturalist. (2021, October 12). Licensed to Share!. iNaturalist. https://www.inaturalist.org/posts/58298-licensed-to-share iNaturalist. (2013, October 8). Observation Location Accuracy. iNaturalist. https://www.inaturalist.org/posts/2035-observation-location-accuracy 49 iNaturalist. (2020, August 4). Research Grade. iNaturalist. https://www.inaturalist.org/posts/39072-research-grade iNaturalist. (2022). What is it? . iNaturalist. https://www.inaturalist.org/pages/what+is+it iNaturalist.ca (2025). Making sure your observations are shared to GBIF. iNaturalist.ca.https://inaturalist.ca/posts/29105-making-sure-your-observations-are-shared-to-gbif iNaturalist.ca. (2023, June 21). What is iNaturalist. iNaturalist.ca. https://inaturalist.ca/pages/what-is-it#:~:text=The%20data%20generated%20by%20the,their%20own%20research%20as%20well. Ivanova, N. V., & Shashkov, M. P. (2021). The possibilities of GBIF data use in ecological research. Russian Journal of Ecology, 52(1), 1-8. https://doi.org/10.1134/S1067413621010069 Lansdown, R.V. (2017). Bidens amplissima. The IUCN Red List of Threatened Species 2017: e.T80603211A80958517. https://dx.doi.org/10.2305/IUCN.UK.2017-3.RLTS.T80603211A80958517.en. Accessed on 22 April 2025. Larsson, T.B. (2001). Biodiversity evaluation tools for European forests. Wiley-Blackwell. p. 178. ISBN 978-87-16-16434-6. Retrieved 28 June 2011. López-Guillén, E., Herrera, I., Bensid, B., Gómez-Bellver, C., Ibáñez, N., Jiménez-Mejías, P., Mairal, M., Mena-García, L., Nualart, N., Utjés-Mascó, M., & López-Pujol, J. (2024). Strengths and challenges of using iNaturalist in plant research with focus on data quality. Diversity (Basel), 16(1), 42. https://doi.org/10.3390/d16010042 50 Marcenò, C., Padullés Cubino, J., Chytrý, M., Genduso, E., Gristina, A. S., La Rosa, A., ... & Guarino, R. (2021). Plant hunting: exploring the behaviour of amateur botanists in the field. Biodiversity and Conservation, 30(11), 3265-3278. Mesaglio, T., & Callaghan, C. T. (2021). An overview of the history, current contributions and future outlook of iNaturalist in australia. Wildlife Research (East Melbourne), 48(4), 289-303. https://doi.org/10.1071/WR20154 McCabe, H. (1999). Vast database offers vision of biodiversity. Nature (London), 400(6739), 5-5. https://doi.org/10.1038/21728 Moulin, N. (2020). When citizen science highlights alien invasive species in france: The case of indochina mantis, hierodula patellifera (insecta, mantodea, mantidae). Biodiversity Data Journal, 8, e46989-e46989. https://doi.org/10.3897/BDJ.8.e46989 Peter, M., Diekötter, T., Höffler, T., Kremer, K., & Mascarenhas, A. (2021). Biodiversity citizen science: Outcomes for the participating citizens. People and Nature (Hoboken, N.J.), 3(2), 294-311. https://doi.org/10.1002/pan3.10193 Pocock, M. J. O., Tweddle, J. C., Savage, J., Robinson, L. D., & Roy, H. E. (2017). The diversity and evolution of ecological and environmental citizen science. PloS One, 12(4), e0172579-e0172579. https://doi.org/10.1371/journal.pone.0172579 Shirey, V., Seppälä, S., Branco, V. V., & Cardoso, P. (2019). Current GBIF occurrence data demonstrates both promise and limitations for potential red listing of spiders. Biodiversity Data Journal, 7, e47369. 51 Song, X., Wambulwa, M. C., Milne, R., Mambo, W. W., Wu, Z., Li, Z., Luo, Y., Vu, D. D., Cai, J., Zhu, G., Li, Z., Cai, L., Li, D., & Liu, J. (2024). Deep plastid phylogeographic divergence of beaked hickory (carya sinensis) calls for transboundary conservation in a biodiversity hotspot. Tree Genetics & Genomes, 20(6), 50-50. https://doi.org/10.1007/s11295-024-01683-5 Sterner, B. W., Gilbert, E. E., & Franz, N. M. (2020). Decentralized but globally coordinated biodiversity data. Frontiers in Big Data, 3, 519133. Unger, S., Rollins, M., Tietz, A., & Dumais, H. (2021). iNaturalist as an engaging tool for identifying organisms in outdoor activities. Journal of Biological Education, 55(5), 537-547. https://doi.org/10.1080/00219266.2020.1739114 Vohland, K., Land-zandstra, A., Ceccaroni, L., Lemmens, R., Perelló, J., Ponti, M., Samson, R., Wagenknecht, K., OAPEN, Springer Nature - Springer Literature, Cultural and Media Studies eBooks 2021 English International, DOAB: Directory of Open Access Books, SpringerLink (Online service), & SpringerLink Fully Open Access Books. (2021). In Vohland K., Vohland K., Land-zandstra A., Land-zandstra A., Ceccaroni L., Ceccaroni L., Lemmens R., Lemmens R., Perelló J., Perelló J., Ponti M., Ponti M., Samson R., Samson R., Wagenknecht K. and Wagenknecht K.(Eds.), The science of citizen science (1st 2021.;1;1st 2021; ed.). Springer International Publishing. https://doi.org/10.1007/978-3-030-58278-4 Wilson, J. S., Pan, A. D., General, D. E. M., & Koch, J. B. (2020). More eyes on the prize: An observation of a very rare, threatened species of philippine bumble bee, bombus irisanensis, on iNaturalist and the importance of citizen science in conservation biology. Journal of Insect Conservation, 24(4), 727-729. https://doi.org/10.1007/s10841-020-00233-3
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eng
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Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Undergraduate
University of British Columbia. ENVR_V 448
Understanding how iNaturalist data affect GBIF and its limitations : A guide to key considerations
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